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A turnaround in healthcare system in the future with AI and ML
The era of superhumans or super-machines!
Machines that will have the ability to think like a human mind, solve problems, learn from experience without being programmed just like humans who is in the constant process of improvement to get the best solution. Best known as Machine Learning (ML), a branch of Artificial Intelligence (AI).
Now, ML is not restricted within the boundary of a techie’s topic of discussion, the concept has further expanded in leaps and bounds to a much wider horizon which includes scientists, researchers and doctors who are working on the path of understanding how ML is going to transform healthcare within a span of years to give people a better life. Till date, the evolving fields of ML, cloud computing, next-generation DNA sequencing and medical imaging have developed in parallel lines. Their rendezvous will open up a new era of biology with an in-depth understanding of the biological processes thereby letting researchers and the medical fraternity to dive deep into the etiology of a disease, its molecular parameters and patient-friendly treatment with personalized drugs.
A new era of healthcare infrastructure and a deep-rooted understanding of the life processes is about to happen. This will help in decoding more polished and powerful approaches to disease prevention, diagnosis and treatment. A convergence of world’s greatest scientists and techies could bring forth best solutions to problems which were thought impossible earlier.
A completely new field of medicine that could benefit human health in ways we cannot even begin to imagine.
The urgency to bring an end to the deadly coronavirus has compelled clinical researchers & scientists to move at lightning speed and get vaccines from bench to bedside within a year. A vaccine usually takes a minimum of 10 years of large scale clinical trials and assessments before it finally hits the market for usage. A very onerous process. This proves that extensive scientific research and innovation must be one of our greatest investments that we can make in our future.
The role of ML algorithms in the realm of healthcare :
Disease prediction and early diagnosis-
Huge piles of electronic data in the healthcare industry have made symptoms analysis and early high-risk disease identification very challenging for the medical fraternity. Here, ML algorithms come to play. Various algorithms like Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and Convolutional Neural Networks (CNN) have played major roles in the prediction of heart diseases, precision breast cancer, kidney diseases and so on, as per findings.
- These algorithms use various statistical and probabilistic techniques to learn from past data and apply it in problem-solving just like humans.
- They also aid in decoding any hidden information from data that might have been overlooked.
- All sorts of high-dimensional, semi-structured and unstructured data can be handled.
- Burden on the healthcare workers can be decimated; survival rate of the patients can be improved.
For instance, approx. 415M people are diabetic worldwide out of which 79M belongs to South-East Asia. Google has tied up with two Eye hospitals in India (Madurai & Chennai) on an endeavor to detect diabetic retinopathy (fastest growing cause of blindness in diabetics) early.
How does it work?
A ML algorithm will analyze the scans of the retinas thereby grading the images on a 5-point scale ranging between NDR (no-diabetic retinopathy) and DR (diabetic retinopathy). When there are probabilities of developing DR, then those patients can be managed by a combination of diet & medication, instead of a surgery.
Accelerated drug development pipelines and generation of new treatment protocols –
As we know, new drug development is a very lengthy and expensive process as it has to undergo clinical trials (Phase 0 — Phase III, Phase IV is optional) before venturing into the market. Pharma giants face problems in getting new drug approvals in lesser time and also focus on the most profitable diseases like cancer, diabetes and Alzheimer’s rather than anti-infectives. In fact, the number of active drug pipelines for cancer has increased by 7.6% with a decrease in anti-infectives by 9.3% because the drug development cost does not meet the selling profits of anti-infective drugs.
This is where AI & ML come to the forefront. The above numbers can be improved by automation of vital data processing & analysis by ML methods, thereby accelerating the drug development pipelines less prone to human-related errors. The advantages of ML integration into drug development are:
- Reduction of drug development costs.
- Uncover new drug targets.
- Patient-friendly therapies.
- Automated search for patient-oriented treatment free of human mistakes.
Drug discovery — The nascent stage of drug development is the most crucial step behind identification of drug targets, causes of a disease. A recent breakthrough in protein design has happened using generative Deep Learning (DL) like GANs (Generative Adversarial Networks) in which two neural networks named Generator and Discriminator compete to become more accurate in their predictions and decision making.
Drug repositioning — New drug design comes with big challenges which has turned tables by generating interest on more profitable and efficient technique called repurposing of ‘old drugs’ whereby available drugs are further studied for new medical uses (therapeutic indications). Currently nearly one-third of the drug approvals belong to the same technique and they generate approx. 30% of the yearly revenue for the pharma industry.
In fact, COVID-19 provided with a good opportunity for the application of AI algorithms for drug repositioning. For example, remdesivir is one such drug that was originally discovered for the treatment of Ebola and has also shown promise in Covid-19.
The era of personalized or precision medicine –
Wondering what is meant by precision medicine? This refers to tailormade therapy for a specific patient by considering parameters like genotype, transcriptomic profiles etc. In oncology, AI can be used to develop a drug combination based on a patient’s own biopsy; also proven beneficial in radiology by detection of abnormal lesions that may progress into cancer.
ML has also been applied to nutrition field to personalize a diet to manage diet-related health issues.
For cardiac problems, AI diagnoses the chances of developing atrial fibrillation that has 10%-20% lifetime risk.
We are on the verge of entering a revolutionary period in which the digitization of human biology and the magic of cloud computing and AI will produce unimaginable breakthroughs in human health management.
A better future is awaiting.
Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot